Effects of Undaria pinnatifida-derived brown algae polysaccharide (UPS) on the nutritional composition, digestive capacity, immune performance and intestinal microbiota of juvenile sea cucumber (Apostichopus japonicus)

Experimental materials

The sea cucumber feed was purchased from Shandong Xuchang Biotechnology Co., Ltd. The compound feed additive “Microbial brick 85” (containing Bacillus subtilis, Lactobacillus acidophilus, and Saccharomyces cerevisiae) was purchased from Shandong Ruilisheng Pharmaceutical Co., Ltd. The brown algae polysaccharide extracted from Undaria pinnatifida (UPS) was produced by Rongcheng Hongde Marine Biotechnology Co., Ltd., with detailed components listed in Table 1. The UPS has an ash content of 58.9%, and the remaining polysaccharide components are alginate, mannitol, fucoidan and fucoheterosaccharides.

Feed fermentation and mixture preparation

Microbial Brick 85 (2% w/w) was added to the sea cucumber feed and fermented at 20 °C for 2 days. The fermented mixture was then blended with marine sediment at a ratio of 1:2.7 (feed:sediment) for feeding. To investigate the effects of UPS dosage on juvenile sea cucumber growth, six experimental groups were established with UPS supplementation levels of 0%, 0.5%, 1%, 1.5%, 2%, and 3% (polysaccharide/sea cucumber biomass), designated as HS0, HS1, HS2, HS3, HS4, and HS5, respectively.

Acclimation and feeding experiment

Juvenile sea cucumbers were acclimated at a density of 40–50 individuals in 10 L filtered seawater for 6 days. Environmental conditions were maintained at 15 ± 1 °C, salinity of 31–33‰, pH 8.0, and dissolved oxygen levels of 10 mg/L. During the acclimation period, the animals were fed daily at 16:00 with a fermented feed mixture equivalent to 5% of their body weight. Residual feed and fecal matter were removed at 14:00 the following day using a plastic tubing siphon. Water exchange was performed as needed, with replacement volumes adjusted based on feeding activity and water quality parameters. As for the feeding experiment, a total of 6 kg of healthy, well-developed, symmetrical, and disease-free juvenile sea cucumbers were selected and were randomly distributed into 18 glass experimental tanks, with 0.33 kg per tank. There were three replicates in each experimental group. Each sea cucumber was blotted on dry filter paper for 30 s and weighed (recorded as W0) before being reintroduced into seawater. Growth and feeding conditions were the same as acclimation parameters.

Sampling

Following a 60-day diet, the juvenile sea cucumbers were collected after 72 h of fasting. All sea cucumbers from each tank were collected and kept on dry filter paper for 30 s to remove excess water. The sea cucumbers were weighed (WT), and the specific growth rate (SGR) was calculated. After weighing, 10 sea cucumbers were randomly selected from each tank and dissected on a chilled surface. Sterilized scissors were used to collect the coelomic fluid of 3 sea cucumbers in each tank and transferred into 1.5 mL centrifuge tubes for immunological function analysis. The intestinal tract of the other three sea cucumbers in each tank were placed into 1.5 mL centrifuge tubes for digestive enzyme activity assays. The intestinal tract of the remaining three sea cucumbers were quickly put into 1.5 mL centrifuge tubes and frozen in dry ice for 16S rRNA sequencing, and the body wall were used to determine the crude composition. The coelomic fluid, intestinal tract and body wall of the last 1 sea cucumber was collected and stored in −80 °C as the backup sample. The body wall weight (WB) and length (L) of the juvenile sea cucumbers were accurately measured. The intestinal tract was stretched to measure its weight (WI) and length (LI) after separation on a chilled surface. All samples were stored at −80 °C immediately after collection.

Nutritional composition analysis

The chemical composition of the juvenile sea cucumber body wall was determined according to AOAC (2005) standard procedures. The samples were dried at 105 °C to determine the moisture content. Protein content in the intestine and coelomic fluid was analyzed using the Coomassie brilliant blue method (reagent purchased from Nanjing Jiancheng Bioengineering Institute, Nanjing, China), while protein content in the body wall was measured via the Kjeldahl method (JK9870 apparatus; Jinan Precision Analytical Instrument Co., Ltd., Jinan, China). Lipid content was determined by Soxhlet extraction. Polysaccharide content was quantified using the phenol-sulfuric acid method. Ash content was determined by incineration in a muffle furnace at 550 °C.

Digestive enzyme activity and immunoenzyme assays

Intestinal digestive enzyme activities were measured using commercial kits: lipase (LPS, A054-1-1), pepsin (PP, A080-1-1), amylase (AMS, C016-1-1), and cellulase (CL, A138-1-1). Under ice bath conditions, 10% sea cucumber intestinal homogenate was prepared, centrifuged at 4,000 rpm for 10 min, and the supernatant was collected for use. The reaction system was prepared according to the kit operation manual, and the activity of each digestive enzyme was determined. For the determination of lipase activity, the absorbance was measured at 420 nm after the reaction system was prepared; for pepsin and amylase activity, the absorbance was measured at 660 nm after reaction solution was incubated at 37 °C for 20 min (pepsin) or 7.5 min (amylase); for cellulase activity, the reaction system was incubated at 37 °C for 30 min, and then immediately boiled for 15 min to terminate the reaction, the absorbance was measured at 550 nm to calculate cellulase activity. Coelomic fluid immune-related parameters, including superoxide dismutase (T-SOD, A001-1-2), total antioxidant capacity (T-AOC, A015-1-2), malondialdehyde (MDA, A003-1-1), and hydrogen peroxide (H₂O₂, A064-1-1), were analyzed with corresponding assay kits. For the determination of T-SOD activity, the reaction solution was prepared according to the kit instructions, incubated at 37 °C for 40 min, and the absorbance was measured at 550 nm; for T-AOC capacity, the reaction solution reacted at 37 °C for 30 min and measured at 520 nm; for the determination of MDA content, the reaction solution was mixed evenly, incubated at 95 °C for 40 min, centrifuged at 4,000 rpm for 10 min, and the supernatant was taken and the absorbance was measured at 532 nm; for the measurement of hydrogen peroxide content, the absorbance was determined at 405 nm. All kits were purchased from Nanjing Jiancheng Bioengineering Institute.

16S rRNA sequencing and bioinformation analysis

The intestinal microbial DNA of juvenile sea cucumbers was extracted using the MagPure Soil DNA LQ Kit (Magen), followed by amplification of the 16S rRNA gene V3–V4 region with primers 343F and 798R. The PCR products were purified with AMPure XP beads and sequenced on the Illumina NovaSeq 6000 platform to generate 250-bp paired-end reads. Preprocessing of paired-end reads was conducted using Cutadapt software to identify and remove adapter sequences. The reads underwent quality filtering, denoising, merging, and chimeric read detection and removal with DADA2 with the default parameters of QIIME2, resulting in representative reads and ASV abundance data. Subsequently, all representative reads were annotated and blasted against the Silva database (Version 138), utilizing q2-feature-classifier with its default parameters. Sequencing services were provided by Shanghai Ouyi Biotechnology Co., Ltd. (Shanghai, China).

ASV flower plot was performed using the OECloud tools at https://cloud.oebiotech.com.

The data was normalized using the following steps. Firstly, the data were converted to proportions by dividing the reads of each ASV in the sample by the total reads in the sample, and analyzed using relative abundance. Then the data were pooled by diluting the data by randomly resampling each sample to the minimum sequencing depth of the sample. Alpha and beta diversity analyses were conducted in QIIME 2. Alpha diversity indices (Chao1, ACE, Good’s coverage, and Simpson) were calculated. One-way analysis of variance (ANOVA) was applied to evaluate the significant differences in the bacterial α-diversity indices between the communities. Principle coordinate analysis (PCoA) by R package was used to determine the differences in the bacterial community structures based on Bray-Curtis distances. Permutational multivariate analysis of variance (PERMANOVA) was performed using the Adonis function to evaluate the effects of UPS supplementation on the variations in the bacterial communities. Significant analysis of differential microbial species at family and genus levels were tested using Kruskal Wallis and P < 0.05 was considered statistically significant. Linear discriminant analysis effect size (LEfSe) analysis was used to determine the microbial taxa that best characterized each study group. Linear discriminant analysis (LDA) scores > 2.0 and P < 0.05 were considered significantly. Functional profiling of the intestinal microbiota was predicted with PICRUSt2 (Yan et al., 2023). Differential metabolic pathways were analyzed using the Kruskal-Wallis test.

Pearson correlation analysis was applied to evaluate associations between microbial taxa, biochemical indicators, and intestinal microbial communities. 0.1 < |Pearson-r| < 0.3 was considered as weak correlation; 0.3 < |Pearson-r| < 0.5 was medium correlation and significant correlations were defined as |Pearson-r| > 0.5 with P < 0.05.

Statistical analysis

SPSS 22.0 (SPSS Inc., Chicago, IL, USA) was used to analyze the test of their normality and homoscedasticity. All results were presented as mean ± S.E.M (n = 3 or 5). One-way analysis of variance (ANOVA) was used for multi-group comparison, and Tukey test was used. P < 0.05 was considered statistically significant. Growth data were calculated, including the weight gain rate ( $\mathrm{W}\mathrm{G}\mathrm{R}\mathrm{\%}=\left(\mathrm{W}\mathrm{T}-\mathrm{W}0\right)/\mathrm{W}0\times 100$), specific growth rate ( $\mathrm{S}\mathrm{G}\mathrm{R}\mathrm{\%}=\left(\mathrm{L}\mathrm{n}\mathrm{W}\mathrm{T}-\mathrm{L}\mathrm{n}\mathrm{W}0\right)/\mathrm{n}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r}\mathrm{o}\mathrm{f}\mathrm{d}\mathrm{a}\mathrm{y}\mathrm{s}\times 100$) (Li et al., 2024), the intestinal body wall weight ratio ( $\mathrm{R}=\mathrm{W}\mathrm{I}/\mathrm{W}\mathrm{B}\times 100$) and the intestinal wall length ratio ( $\mathrm{R}\mathrm{G}\mathrm{L}=\mathrm{L}\mathrm{I}/\mathrm{L}\times 100$). Data visualization processing was performed using Origin 2024.

Effects of UPS on the growth index

After 60 days of feeding, there was no significant difference in the weight gain rate WGR, SGR, R and RGL (Table S1) indicating that UPS have few effects on the growth of juvenile sea cucumbers.

Effects of UPS on the nutritional composition

Analysis of the nutritional components in juvenile sea cucumbers revealed that the polysaccharide content of body wall in HS2 group exhibited approximately 1.6-fold higher than that in HS0 group (P < 0.05). However, UPS supplementation had no significant effect on protein, lipid, ash, or moisture content in the body wall (P > 0.05) (Table 2). These results suggested that low UPS supplementation (at 1% concentration) could enhance polysaccharide accumulation in juvenile sea cucumbers, thus improving quality of sea cucumbers.

Effects of UPS on the intestinal digestive enzyme activity

The activities of cellulase, lipase, pepsin, and amylase of the intestinal tract of juvenile sea cucumbers were measured. As demonstrated in Table 3, the HS2 group exhibited significantly higher cellulase activity than HS0 and other experimental groups (P < 0.05), which was 5.8-fold higher than that of HS0 (Table 3). Additionally, lipase activity in all UPS-supplemented groups exceeded that of HS0, with HS5 group showing 1.6-fold higher than HS0 group. However, no significant differences in pepsin or amylase activities were observed between experimental and control groups (Table 3). These results showed that low UPS supplementation (1%) could increase the cellulase activity and UPS gradually increased the lipase activity of sea cucumbers with the increase of concentration.

Effects of UPS on the immune performance

To investigate the effects of UPS on immune performance in juvenile sea cucumbers, the activity of superoxide dismutase (T-SOD), total antioxidant capacity (T-AOC), and the contents of malondialdehyde (MDA) and hydrogen peroxide (H₂O₂) in coelomic fluid were analyzed. As shown in Table 4, T-AOC kept unchnaged with the increase of UPS concentration. The T-SOD activities in all UPS-supplemented groups were significantly higher than in HS0 (P < 0.05), with the HS2 and HS3 groups showing the most pronounced increase (1.4-fold higher than HS0, P < 0.05). Conversely, MDA content in low-UPS supplementation group (HS3) was significantly lower than that in HS0, whereas in high-UPS groups (HS4 and HS5) was higher. These results indicated that UPS supplementation especially with low UPS concentration could obviously improve the immune performance of juvenile sea cucumbers. However, the content of H₂O₂ in all UPS supplementation groups were higher than HS0 group indicating that UPS supplementation could also lead to oxidative stress in some degree.

Effects of UPS on the intestinal microbiota diversity

As shown in Fig. 1A, no significant difference in ASV counts was observed between the control group (HS0) and UPS-supplemented groups (P > 0.05) (Fig. 1A). The rarefaction curves tend to flatten as the number of extraction sequences increases indicating that the amount of sequencing data of samples was reasonable (Fig. S1). The β-diversity was analyzed using Bray-Curtis distance. The intestinal bacterial communities (PERMANOVA, R = 0.692 and P = 0.034 < 0.05) were significantly differentiated based on UPS supplementation (Fig. 1B), indicating that UPS supplementation could affected the intestinal bacterial communities of juvenile sea cucumbers.

Alpha diversity across groups was assessed using the ACE index, Chao1 index, Good’s coverage, and Simpson index. As demonstrated in Fig. 2, both ACE and Chao1 indices in UPS-supplemented groups showed non-significant decreases compared to HS0. Although the Simpson index significantly declined in the HS5 group (P < 0.05), UPS supplementation exhibited no significant impact on the alpha diversity of intestinal microbiota in juvenile sea cucumbers in other groups (Fig. 2).

Analysis of intestinal microbial composition

“Microbial brick 85” (containing Bacillus subtilis, Lactobacillus acidophilus, and Saccharomyces cerevisiae) was used to ferment the feed of sea cucumber. In order to detect its effect of intestinal bacterial communities, the intestinal microorganisms of sea cucumber were cultured. The result revealed that only one strain of Bacillus subtilis was obtained from each of the HS1, HS2, and HS4 groups in MRS medium, while no Lactobacillus acidophilus was isolated (Table S2). In addition, it was found that these three strains had poor salt tolerance and could not be obtained on the seawater culture medium, indicating that the microbial strains of the brick could not survive in the sea cucumber breeding environment, so the influence on the intestinal microbial community of the sea cucumber could be ignored.

The intestinal microbiota composition of juvenile sea cucumbers at the phylum level is shown in Fig. 3A. In HS0 and UPS-supplemented groups, the dominant phyla were primarily Bacteroidota, Proteobacteria, Firmicutes, and Actinobacteriota. No significant differences in microbial abundance were observed among these groups (Fig. 3A). At the family level, families with an average relative abundance > 1% were analyzed. As shown in Fig. 3B, the dominant families in all groups included Muribaculaceae, Rhodobacteraceae, Prevotellaceae, Vibrionaceae, and Lachnospiraceae. The highest relative abundance of Muribaculaceae (24.1%) was observed in HS0, while all UPS-supplemented groups exhibited lower values, with the lowest in HS5 (12.5%). The abundance of Rhodobacteraceae was higher in all groups compared to HS0 except HS4. No significant differences in Prevotellaceae abundance were detected between the control and UPS-supplemented groups. Vibrionaceae abundance varied markedly among groups, ranging from 0.9% in HS2 to 18.9% in HS1 (Fig. 3B). At the genus level, the dominant genera across groups were Muribaculaceae, Prevotellaceae UCG-001, Vibrio, and Alistipes (Fig. 3C).

Differential microbial species among groups were analyzed at the family and genus levels. As shown in Fig. 4A, the differential families were EV818SWSAP88, Burkholderiaceae, Peptostreptococcales-Tissierellales, unknown family and BD2-7 (P < 0.05). It is worth noting that the abundance of Burkholderiaceae family strains was correlated with the amount of UPS added. In low UPS supplementation groups (HS1, HS2 and HS3), the abundance of Burkholderiaceae family strains decreased with the improvement of UPS concentration while the high-concentration addition group showed an opposite trend (Fig. 4A). At the genus level, Genera with significant intergroup differences in relative abundance included EV818SWSAP88, Ralstonia, Lachnospiraceae UCG-001, Finegoldia, and BD2-7 (Fig. 4B). The abundance of Ralstonia strains showed the same trend with the Burkholderiaceae family strains , and their abundance was closely related to UPS concentration. Morever, the abundance of Lachnospiraceae UCG-001 strains increased in HS3, HS4 and HS5 groups. The result of LEfSe analysis was consistent with Figs. 4A, 4B showing the high abundance of Burkholderiaceae family and Ralstonia strains in HS0 (Fig. 4C). These results demonstrated that low UPS supplementation could significantly reduce the relative abundance of the pathogenic microorganisms (Ralstonia) and change the microbial structure of juvenile sea cucumbers.

Functional analysis of intestinal microbiota

Functional prediction analysis using PICRUSt2 revealed microbial functional profiles at three hierarchical levels. At level 1, the control group (HS0) and experimental groups shared six functional categories: cellular processes, environmental information processing, genetic information processing, human diseases, metabolism, and organismic systems. Among these, metabolism was the predominant category, though no significant intergroup differences in functional abundance were observed (Fig. S2A). At level 2, key functional pathways included amino acid metabolism, carbohydrate metabolism, cellular community, and energy metabolism. While minor variations occurred in cell motility and signal transduction, there were no significant differences in other functional pathways (Fig. S2B). Further functional analysis was conducted at the level 3, showing mainly some differences in the three metabolic pathways such as nitrotoluene degradation, nonribosomal peptide structures, and pentose and glucuronate interconversions.

With increasing UPS concentration (excluding HS1), these pathways exhibited progressive enhancement. The HS5 group reached the highest level. However, no significant differences were detected between HS2, HS3, HS4, and the control (Fig. S2C).

Correlation analysis

The results of Pearson correlation analysis of five significantly divergent microorganisms with nutritional composition, immunity index and digestive enzyme activities were shown in Fig. 5. The Ralstonia strain had a significant negative correlation with the H₂O₂ content (r = −0.51, P < 0.05), the protein content (r = −0.55, P < 0.05) and the T-SOD enzyme activity (r = −0.65, P < 0.01). Lachnospiraceae UCG-001 (r = 0.59, P = 0.01) and Finegoldia (r = 0.59, P = 0.01) had a significant positive correlation with T-AOC capacity, and BD2-7 significantly correlated with protein content (r = 0.59, P = 0.01).